Systems and methods for allocating hydrocarbon production values

ABSTRACT

Techniques for allocating hydrocarbon production include receiving a selection of a particular area identification (ID) of a plurality of area IDs stored on the server; determining based on the selected particular area ID, a plurality of hydrocarbon production values that include periodic area-level hydrocarbon production values associated with the particular area ID and a plurality of wells associated with the particular area ID; determining a decline curve model for the area-level hydrocarbon production values associated with the particular area ID; modeling the aggregated periodic well-level hydrocarbon production values with the determined decline curve model; and determining allocated well-level hydrocarbon production values based at least in part on the selected decline curve model to display at a client device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority under 35U.S.C. § 120 to, U.S. patent application Ser. No. 16/534,436, filed onAug. 7, 2019, which is a continuation-in-part of, and claims priorityto, U.S. patent application Ser. No. 16/418,019, filed on May 21, 2019,which in turn is a continuation of, and claims priority under 35 U.S.C.§ 120 to, U.S. patent application Ser. No. 15/247,097, filed on Aug. 25,2016, now U.S. Pat. No. 10,303,819. The entire contents of all priorapplications are incorporated by reference herein.

TECHNICAL FIELD

This document relates to systems and methods for allocating hydrocarbonproduction values and, more particularly, allocating area-level, orlease-level, hydrocarbon production values to one or more hydrocarbonwells located on the area or lease.

BACKGROUND

Periodic hydrocarbon (e.g., oil, gas) and water production fromproducing wells are reported to state agencies (e.g., the Texas RailroadCommission) for recordal and informational purposes. Often, the reportedhydrocarbon and water production is reported as an aggregated value fora particular geographic or legally-defined area. Within the particulargeographic or legally-defined area, there may be many producing wellsthat contribute to the aggregated periodic reported values. That is,whether the area includes a single well or many wells, only aggregatedproduction values are reported. For multi-well areas, it may bedifficult to determine periodic production values on a well-by-wellbasis. For example, allocation of the aggregated periodic values amongthe multiple wells may be dependent, for example, on which wells areproducing when and for how long.

SUMMARY

In an example implementation for allocating hydrocarbon production,computer-implemented methods, systems, and computer-readable media forallocating hydrocarbon production include the steps or operations of (i)receiving, from a client device communicably coupled to a server thatincludes one or more processors, a selection of a particular areaidentification (ID) of a plurality of area IDs stored on the server;(ii) determining, with the one or more processors, based on the selectedparticular area ID, a plurality of hydrocarbon production values thatinclude periodic area-level hydrocarbon production values associatedwith the particular area ID and a plurality of wells associated with theparticular area ID; (iii) determining, with the one or more processors,a decline curve model for the area-level hydrocarbon production valuesassociated with the particular area ID; (iv) modeling, with the one ormore processors, the aggregated periodic well-level hydrocarbonproduction values with the determined decline curve model; and (v)determining, with the one or more processors, allocated well-levelhydrocarbon production values based at least in part on the selecteddecline curve model to display at the client device.

In an aspect combinable with the example implementations, determining adecline curve model for the area-level hydrocarbon production valuesassociated with the particular area ID includes determining the declinecurve model for the area-level hydrocarbon production values associatedwith the particular area ID based, at least in part, on a geology of areservoir associated with the area ID.

In another aspect combinable with any of the previous aspects,determining a decline curve model for the area-level hydrocarbonproduction values associated with the particular area ID based, at leastin part, on a geology of a reservoir associated with the area IDincludes (vi) determining, with the processor, allocated periodicwell-level hydrocarbon production values for each of the plurality ofwells associated with the particular area ID; (vii) shifting, with theprocessor, the allocated periodic well-level hydrocarbon productionvalues for each of the plurality of wells to an initial time period;(viii) aggregating, with the processor, the shifted allocated periodicwell-level hydrocarbon production values to generate aggregated periodicarea-level hydrocarbon production values; and (ix) determining thedecline curve model for the area-level hydrocarbon production valuesassociated with the particular area ID based on the aggregated periodicwell-level hydrocarbon production values.

Another aspect combinable with any of the previous aspects furtherincludes determining a number of periods associated with the particulararea ID; determining a first period of the number of periods, the firstperiod associated with a first area-level hydrocarbon production value;and determining a last period of the number of periods, the last periodassociated with a last area-level hydrocarbon production value.

Another aspect combinable with any of the previous aspects furtherincludes selecting a period of the number of periods, starting with thefirst period and ending with the last period; for the selected period,determining a total number of wells associated with the particular areaID; and for the selected period, determining a number of active wellsassociated with the particular area ID.

Another aspect combinable with any of the previous aspects furtherincludes, based on the number of active wells being one active well inthe selected period, assigning an area-level hydrocarbon productionvalue in the selected period to the one active well.

Another aspect combinable with any of the previous aspects furtherincludes, based on the number of active wells being more than one activewell, for each active well in the selected period: determining that theactive well has at least one of pending production or sales productionin the selected period; and based on the active well having the at leastone of pending production or sales production in the selected period,assigning the at least one of pending production or sales production tothe active well.

Another aspect combinable with any of the previous aspects furtherincludes determining that the active well has no pending production andno sales production in the selected period; based on the active wellhaving no pending production and no sales production in the selectedperiod, determining that the active well has an assigned decline curvemodel; and based on the active well having the assigned decline curvemodel, determining a predicted production for the selected period forthe active well.

Another aspect combinable with any of the previous aspects furtherincludes based on the selected period being subsequent to the firstperiod, determining that the active well is associated with a predictedproduction from the assigned decline curve model from a previous periodin the number of periods; proportioning the predicted production of theactive well for the selected period based on the predicted production ofthe active well for the previous period; and assigning the proportionedpredicted production to the active well for the selected period.

Another aspect combinable with any of the previous aspects furtherincludes determining that the active well has no pending production andno sales production in the selected period and no assigned decline curvemodel; and based on the determination, flagging the active well as a newwell for the selected period.

Another aspect combinable with any of the previous aspects furtherincludes, for each new well in the selected period: determining a sumof: (a) pending production for the active wells in the selected periodor sales production for the active wells in the selected period, and (b)the predicted production for the active wells in the selected period;determining that the sum is greater than the area-level hydrocarbonproduction value for the selected period; equalizing the sum of (a) and(b) with the area-level hydrocarbon production value for the selectedperiod; and assigning zero production to each new well for the selectedperiod.

Another aspect combinable with any of the previous aspects furtherincludes determining that the sum of (a) and (b) is less than thearea-level hydrocarbon production value for the selected period; anddetermining a difference between the sum of predicted production for theactive wells in the selected period and the predicted production for theactive wells in the selected period and the area-level hydrocarbonproduction value for the selected period; and assigning, to each of thenew wells in the selected period, a proportional hydrocarbon productionvalue based on the difference.

Another aspect combinable with any of the previous aspects furtherincludes, for each new well in the selected period: determining that theassigned proportional hydrocarbon production value to the new well inthe selected period is less than an assigned proportional hydrocarbonproduction value to the new well in a previous period; identifying welltest data associated with the new well; and based on the identified welltest data, fitting the decline curve model to the new well based atleast in part on the well test data and the assigned proportionalhydrocarbon production values of the new well in the selected period andthe previous period.

Another aspect combinable with any of the previous aspects furtherincludes identifying no well test data associated with the new well; andbased on the identification of no well test data associated with the newwell, fitting the decline curve model to the new well.

Another aspect combinable with any of the previous aspects furtherincludes adjusting the decline curve model for the new well based, atleast in part, on the assigned proportional hydrocarbon production valueof the new well in the previous period.

Another aspect combinable with any of the previous aspects furtherincludes identifying no new wells in the selected period; and based onthe identification of no new wells in the selected period and based onthe selected period being the first period, assigning a proportionedpredicted production to the active well for the selected period, theproportioned predicted production based on the decline curve model andthe number of active wells.

In another aspect combinable with any of the previous aspects, the timeperiod includes a month.

In another aspect combinable with any of the previous aspects, thedecline curve model includes an Arp's equation decline curve model.

In another aspect combinable with any of the previous aspects, thedecline curve model is defined, at least in part, by a maximum periodichydrocarbon production value and at least one decline rate.

In another aspect combinable with any of the previous aspects, the atleast one decline rate includes an initial decline rate and a declinerate over time.

Another aspect combinable with any of the previous aspects furtherincludes performing an iterative process of determining the allocatedwell-level hydrocarbon production values by iterating steps (vi)-(ix).

In another aspect combinable with any of the previous aspects, iteratingsteps (vi)-(ix) includes determining new allocated periodic well-levelhydrocarbon production values for each of the plurality of wells basedon the determined allocated well-level hydrocarbon production values ina previous iteration of step (v); shifting the new allocated periodicwell-level hydrocarbon production values for each of the plurality ofwells to a first period of a number of periods associated with theparticular area ID; aggregating the shifted new allocated periodicwell-level hydrocarbon production values to generate new aggregatedperiodic area-level hydrocarbon production values; and determining a newdecline curve model for the new aggregated periodic area-levelhydrocarbon production values.

In another aspect combinable with any of the previous aspects, an areaID includes a lease ID.

Implementations may also include systems or computer programs. Forexample, a system of one or more computers can be configured to performparticular actions by virtue of having software, firmware, hardware, ora combination of them installed on the system that in operation causesor cause the system to perform the actions. One or more computerprograms can be configured to perform particular actions by virtue ofincluding instructions stored on non-transitory computer-readable mediathat, when executed by data processing apparatus, cause the apparatus toperform the actions.

One, some, or all of the implementations according to the presentdisclosure may include one or more of the following features. Forexample, a computer-implemented allocation model may use a deterministicmodel of expected decline for each well among multiple producing wellsin a particular area or lease in order to allocate production values ona well-by-well basis from an aggregated reported hydrocarbon (or water)production. The allocation model may reduce or eliminate artifactswithin the aggregated reported production, such as sudden changes inmonth-to-month production (e.g., spikes, zero-values) that can beinterpreted as operation events (e.g., recompletions, shut-ins) wherenone actually exist. Thus, the allocation model may more accuratelydetermine actual well-by-well production values on a periodic basisbased on reported aggregated production. Further, the allocation modelmay more accurately produce estimated ultimate recovery forecasts (EURs)relative to conventional allocation techniques. As another example, theallocation model may, by using a deterministic model, incorporates theconcept that a local petroleum geology of the producing reservoir haspredictive value in allocating aggregated hydrocarbon production valuesto individual wells contributing to those aggregated values.

One, some, or all of the implementations according to the presentdisclosure may include one or more of the following features. Forexample, the computer-implemented allocation model may increaseefficiencies (e.g., in computing time and resources) relative toconventional allocation techniques by providing for an iterative processthat more quickly (e.g., within a couple or several iterations) reachesallocation values very close to final values. As another example, thecomputer-implemented allocation model may increase efficiencies (e.g.,in computing time and resources) by improving the understanding of thechange in a decline curve model over time as well spacing decreases.This time-dynamic modeling is then used in economic analysis of wellplanning. As another example, the computer-implemented allocation modelmay improve estimates of ultimate recovery or remaining recoverables ata specific future time, which may in turn provide for more efficienteconomic planning of future wells. As another example, thecomputer-implemented allocation model may improve an understanding ofthe impact of the reservoir geology on the economic recovery ofhydrocarbons. Also, the computer-implemented allocation model mayimprove an understanding of remaining recoverable hydrocarbons within agiven resource play (e.g., given formation, lease, or otherwise). Asanother example, the addition of sales production data to the allocationmodel may provide clearer picture of well-level production for a longerperiod of time as compared to conventional models.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example distributed network architecture thatincludes one or more client devices and one or more server devices thatexecute an allocation model according to the present disclosure.

FIGS. 2A-2C, 3A-3B, and 4 illustrate flowcharts that depict an exampleiterative process for allocating hydrocarbon production values on awell-by-well basis for a selected area according to the presentdisclosure.

FIGS. 5A-5G graphically illustrate one or more steps of the exampleiterative process for allocating hydrocarbon production values on awell-by-well basis for a selected area according to the presentdisclosure.

FIG. 6 illustrates an example output from an iterative process forallocating hydrocarbon production values on a well-by-well basis for aselected area according to the present disclosure.

FIGS. 7A-7D graphically illustrate one or more steps of the exampleiterative process for allocating hydrocarbon production values on awell-by-well basis for a selected area according to the presentdisclosure.

DETAILED DESCRIPTION

This document discusses techniques (e.g., computer-implemented method,computer program product, computer system) for executing an allocationmodel that determines allocated hydrocarbon production on a well-by-welland periodic basis from reported aggregated lease-level production. Insome aspects, the allocation model according to the present disclosuremay use a deterministic model of expected decline for each well withinmultiple wells assigned to or associated with a particular area. In someaspects, an “area” may represent an arbitrary geographic area, e.g.,selected by or defined by a user, operator, or owner of an allocationmodel service that executes the allocation model. In other aspects, an“area” may represent or coincide with a legally defined geographic area,e.g., a county, a township, a state, a city, or multiples thereof. Inother aspects, an “area” may represent a hydrocarbon lease. In turn, ahydrocarbon lease may generally represent or define an area of surfaceland on which exploration or production activity of hydrocarbons, water,or both hydrocarbons and water, occurs. In some aspects, a hydrocarbonlease may represent or define a contractually defined area that conveysrights to explore and produce from an owner of mineral rights in thatarea (lessor) to a tenant (lessee), usually for a fee and with aspecified duration.

In some aspects, the allocation model may receive or identify severaldata inputs. The data inputs may be stored (e.g., historical or gathereddata) or provided (e.g., by a user or operator of the allocation modelservice). The data inputs may include, for example, reported aggregatedhydrocarbon (or water) well production for an area, the first and lasthydrocarbon production periods (e.g., time) for each well associatedwith the area, and pending production reported for each well associatedwith the area. Pending production values may include periodichydrocarbon production values for one or more wells in one or moreperiods prior to such wells being associated with the area (e.g., priorto the wells being legally assigned to a lease).

Certain data, e.g., the reported aggregated hydrocarbon productionvalues, may be segmented by time period, or “period.” In some aspects, aperiod may represent one month. In alternative aspects, a period may bea shorter period of time (e.g., a week, a day) or a longer period oftime (e.g., a year, multiple months or years).

In some aspects, data inputs to the allocation model may also includewell-test data. Such well-test data, reported on a well-by-well basis(if available) for the wells associated with the area may be reportedafter completion/recompletion operations or on irregular intervals(e.g., Railroad Commission of the State of Texas Form W-10, Oil WellStatus Report), or both. Typically, these data represent measuredhydrocarbon production data over a 24 hour period. Extrapolating suchdaily production data to a full month value may be potentiallyindicative of monthly production, but may not be completely accurate. Insome aspects, the allocation model may account for this imperfectionwhile still honoring the reported well-test data, along with the pendingproduction values associated with the area.

In some aspects, data inputs to the allocation model may also includewell income data, e.g., for income taxation purposes, to a regulatoryagency such as, for example, a comptroller of accounts agency of a stateor other political or geographical unit. For example, in some aspects,the well income data is “sales production data” reported to a regulatoryagency responsible for maintaining such data by the entity responsiblefor paying taxes on income derived from such sales. For example, salesproduction data may be collected and stored (and then obtained orretrieved) by a regulatory entity that is responsible for collectingmany different taxes at different levels throughout the state (or otherpolitical or geographic unit). For example, the regulatory entity maycollect crude oil and natural gas taxes. In some aspects, the salesproduction data is taxpayer-provided sales production data for purposesof taxing (e.g., provided to the regulatory entity). Thus, the salesproduction data may be differentiated from pending production data,which includes hydrocarbon volumes produced at the lease-level,including some pending volumes actually reported to individual wells ofthe lease even prior to assignment to a lease. As anotherdifferentiator, the sales production data may be data focused ontaxation, so such data includes volumes moved off lease or sold, alongwith dollar values assigned to those volumes moved off lease or sold. Insome aspects, the sales production data includes “pending” values, wheresales volumes are reported to a well for a period of time. In suchcases, a well may remain on pending within the sales production data fora longer period of time than the pending production data (e.g., from arailroad commission entity).

The allocation model may utilize a deterministic decline curve toallocate hydrocarbon production values on a well-by-well basis, based onreported aggregated hydrocarbon production values associated with aparticular area (e.g., lease). For example, the allocation model may usethe Arp's equation or other decline equation (e.g., Duong, Power Law,Logistic Growth, Stretched Exponential, or otherwise). The selecteddecline equation (e.g., Arp's or otherwise) may be characterized by oneor more criteria. For example, in the case of Arp's equation, a selectedor modeled decline curve may be characterized by a maximum hydrocarbonproduction value (Qi), an annualized initial decline rate (D), and adecline rate over time (b). Qi may represent a maximum periodichydrocarbon production value for a particular well over a lifetime ofproduction for the well. D and b may define the producing reservoir(e.g., geologic formation) decline, which are representative of thereservoir's producibility over time. Qi may be more of a function of thelocal geologic variation and any operational variations. Thus, for anarea in which wells associated with that area produce from the same, orat least a homogeneous, reservoir (e.g., sandstone, shale), it may beassumed that D and b are consistent from well to well, while Qi maydiffer from well to well. Further, the decline curve model may be morecomplex to include multiple segments (e.g., terminal decline model,usually exponential or constant decline, to account for changes in flowregime such as transient to boundary-dominant flow).

FIG. 1 illustrates an example distributed network architecture 100 thatincludes one or more client devices and one or more server devices thatexecute an allocation model through an allocation model service. Thenetwork architecture 100 includes a number of client devices 102, 104,106, 108, 110 communicably connected to a server system 112 by a network114. The server system 112 includes a processing device 116 and a datastore 118. The processing device 116 executes computer instructions(e.g., all or a part of an allocation model) stored in the data store118 to perform the functions of the allocation model service. Forexample, in some aspects, the allocation model service may be asubscription service available to the client devices 102, 104, 106, 108,and 110 (and other client devices) by an owner or operator of the serversystem 112. In some aspects, the server system 112 may be owned oroperated by a third party (e.g., a collocation server system) that hoststhe allocation model service for the owner or operator of the allocationmodel service.

Users of the client devices 102, 104, 106, 108, 110 access the serverdevice 112 to participate in the allocation model service. For example,the client devices 102, 104, 106, 108, 110 can execute web browserapplications that can be used to access the allocation model service. Inanother example, the client devices 102, 104, 106, 108, 110 can executesoftware applications that are specific to the allocation model service(e.g., as “apps” running on smartphones). In other words, all of theallocation model service may be hosted and executed on the server system112. Or in alternative aspects, a portion of the allocation modelservice may execute on the client devices 102, 104, 106, 108, and 110(e.g., to receive and transmit information entered by a user of suchclient devices or to display output data from the allocation modelservice to the user, or both).

In some implementations, the client devices 102, 104, 106, 108, 110 canbe provided as computing devices such as laptop or desktop computers,smartphones, personal digital assistants, portable media players, tabletcomputers, or other appropriate computing devices that can be used tocommunicate with an electronic social network. In some implementations,the server system 112 can be a single computing device such as acomputer server. In some implementations, the server system 112 canrepresent more than one computing device working together to perform theactions of a server computer (e.g., cloud computing). In someimplementations, the network 114 can be a public communication network(e.g., the Internet, cellular data network, dialup modems over atelephone network) or a private communications network (e.g., privateLAN, leased lines).

As illustrated in FIG. 1, the server system 112 (e.g., the data store118) may store one or more hydrocarbon production records 120. Eachhydrocarbon production record 120 may be publicly available informationassociated with a particular hydrocarbon production area (e.g., lease orotherwise) and identified by a particular area identification value(“area ID”). In some aspects, an area ID may be a lease name, a countyname, or other identifying characteristic for a group of wells.

For example, each record 120 may include reported aggregated hydrocarbonwell production for the particular area, the first and last hydrocarbonproduction periods for each well associated with the particular area,and pending production reported for each well associated with theparticular area. In some aspects, each hydrocarbon production record 120may include well test data (when available) reported for one or more ofthe wells associated with the particular area.

As another example, each record 120 may include sales production datareported to a regulatory agency responsible for maintaining such data bythe entity responsible for paying taxes on income derived from suchsales. For example, sales production data may be collected and stored(and then obtained or retrieved) by a regulatory entity that isresponsible for collecting many different taxes at different levelsthroughout the state (or other political or geographic unit). Forexample, the regulatory entity may collect crude oil and natural gastaxes. In some aspects, the sales production data is taxpayer-providedsales production data for purposes of taxing (e.g., provided to theregulatory entity).

In some aspects, the hydrocarbon production records 120 may also includeoutput data from the allocation model that are based on, for example,the reported aggregated hydrocarbon well production for the particulararea, the first and last hydrocarbon production periods for each wellassociated with the particular area, and pending production reported foreach well associated with the particular area. Such output data may bepresented, for example by the server system 112, for viewing orotherwise by the client devices 102, 104, 106, 108, and 110.

In some aspects, data in the hydrocarbon production records 120 may bearranged as arrays of time values for each period in the hydrocarbonproduction values associated with the record 120 (e.g., associated withan area ID). In some aspects, each array comprises a doubly-sub scriptedarray of currently allocated production streams where the first index isthe well (e.g., well name or well identification (ID) value) and thesecond index is the period (e.g. month). In cases where a well has noproduction in a given period (e.g., has not yet started production, hasended production, or been temporarily shut-in or suspended), then thestream value for that well in that month is flagged as non-producing.

The hydrocarbon production record 120 may also includedoubly-subscripted array of pending production where the first index isthe well (e.g., well name or well identification (ID) value) and thesecond index is the period (e.g. month). Pending production may alsocontain a zero-value in any period where the well was off-line for theduration of the period (e.g., was shut in or production was suspended).If a well has no pending production in a given period, then the pendingproduction value for that well in that period is flagged as producing,but without a known value. Along with, or in place of the pendingproduction data, the hydrocarbon production record 120 may also includedoubly-subscripted array of well income-derived production data (“salesproduction data”), where the first index is the well (e.g., well name orwell identification (ID) value) and the second index is the period (e.g.month). In some aspects, the sales production data may be determinedaccording to data provided to a taxing agency within a particularpolitical or geographic unit, such as a state or other unit. Forexample, well level or lease level income data in Texas may be reported(e.g., by the well owner or operator or otherwise income beneficiary) toa Comptroller of Accounts. In some examples, the data from theComptroller of Accounts may be reported in terms of a monetary value ofthe production value; thus, the hydrocarbon production record 120 mayinclude sales production data synthesized or otherwise derived from themonetary value (e.g., on a periodic basis). In other example, the datafrom the Comptroller of Accounts may be reported in terms of a volumevalue of the production value; thus, the hydrocarbon production record120 may include the actual volume values (e.g., on a periodic basis).

The hydrocarbon production record 120 may also includedoubly-subscripted array of well test data (e.g., scaled to periodic,such as monthly, values) where the first index is the well (e.g., wellname or well identification (ID) value) and the second index is theperiod (e.g. month). If a well has no well test data in a given period,then the well test value for that well in that period may also beflagged producing, but without a known value.

FIGS. 2A-2C, 3A-3B, and 4 illustrate flowcharts that depict an exampleiterative process for allocating hydrocarbon production values on awell-by-well basis for a selected area with an allocation model 200. Insome aspects, the allocation model 200 may be executed by the serversystem 112 (e.g., the processing device 116). In some aspects, theallocation model 200 may include one or more sub-processes, such asprocesses 300 and 400 illustrated in FIGS. 3A-3B and 4, respectively.

The illustrated implementation of the allocation model 200 may begin atstep 202, which includes receiving a selection of a particular area ID,e.g., from a client device. For example, the allocation model servicemay expose (e.g., in a drop down menu or otherwise) the area IDsassociated with the hydrocarbon production records 120 to the clientdevices 102 . . . 110 for selection. A user of a particular clientdevice may select a particular area ID, with the selection received oracknowledged by the allocation model service on the server system 112.

The allocation model 200 may continue at steps 204 through 208, whichinclude identifying periodic hydrocarbon production values associatedwith the selected area ID, identifying first and last periods ofhydrocarbon production values associated with the selected area ID, andidentifying wells associated with the selected area ID, respectively.For example, the allocation model service may identify or otherwisedetermine the particular hydrocarbon production record 120 that isassociated with the selected area ID. Turning briefly to FIG. 5A, agraphical representation of the area-level (e.g., lease level) periodichydrocarbon production value curve 500 associated with the selected areaID is illustrated. As shown, the curve 500 connects each period's (eachmonth's, represented on the x-axis) aggregate lease-level production(represented in barrels (BBL) on the y-axis) from a first period(November 2012) to a last period (March 2016). In this example, the areaID is associated with the Prost Unit B lease in McMullen County, Tex.,USA. The data shown in FIG. 5A is generated from publicly availablereported production data (as reported to the Railroad Commission of theState of Texas).

Data stored in or associated with the particular hydrocarbon productionrecord 120 may, therefore, also be identified or otherwise determined.Such data, as previously described, may include reported aggregatedhydrocarbon well production for the selected area ID, the first and lasthydrocarbon production periods for each well associated with theselected area ID, and pending production reported for each wellassociated with the selected area ID.

The allocation model 200 may continue at step 210, which includesallocating the periodic hydrocarbon production values among theidentified wells on a periodic basis. For example, while the hydrocarbonproduction record 120 (e.g., based on or including reported,publicly-available production information) may include hydrocarbonproduction values for the selected area ID (e.g., for the selectedlease), such reported values may only be at an area-level (e.g.,lease-level) rather than for individual wells associated with theselected area. Thus, the allocation model 200 may determine allocatedwell-by-well periodic production values.

FIG. 2B illustrates a particular implementation of step 210 as shown insteps 212-through 222. Steps 212-222 of the allocation model 200 may beexecuted, therefore, in order to execute step 210 of the model 200. Step212 includes identifying a first period associated with selected areaID. For example, in some aspects, the first period may include a firstmonth in which the area ID included hydrocarbon production for at leastone well associated with the area ID.

The allocation model 200 continues at step 214, which includesdetermining a number of active wells in the identified period. An activewell, for example, includes a well associated with the area ID that isalso associated with the identified period within the hydrocarbonproduction record 120. For instance, typically, an active well is a wellwhich includes hydrocarbon production included in the hydrocarbonproduction value associated with the selected area ID for the identifiedperiod. Not all wells associated with the selected area ID may be activefor each period (e.g., from first to last period), as not every well mayproduce hydrocarbon in every period or not every well may go “online”(e.g., produce hydrocarbons) in the same period (e.g., wells go onlinein “staggered” periods), or both.

In step 216, a determination is made whether there is one active well inthe identified period or more than one active well in the identifiedperiod. If there is a single active well associated with the area ID inthe identified period, then, in step 218, the hydrocarbon value for theidentified period is assigned (e.g., allocated) to the single activewell. In other words, in the case of only one active well reportedduring a particular period (e.g., month), then all reported hydrocarbonproduction for the identified period is assigned or allocated to thatwell.

In step 220, a determination is made whether the identified period isthe last period (e.g., month) associated with the area ID. In someaspects, for example, an area ID may have many periods of production,such as years or decades. If the identified period is the last periodassociated with the area ID, then the allocation model 200 continues inan iterative process through sub-process 400 (explained in more detailbelow). Otherwise, the next period associated with the area ID (e.g.,the next month) is identified in step 222 and the model 200 returns tostep 214 to loop through steps 216-222.

As is often the case, there may be more than one active well reportedfor the selected area ID for the identified period. Thus, if there aremore than one active well, step 216 continues at step 224, shown in FIG.2C. Generally, and as explained in more detail below, when more than oneactive well is contained in an area-level aggregate hydrocarbonproduction value for the identified period, the allocation is based oneither a predicted production value from the decline curve for eachwell, or for proportional allocation to the well if no decline curve yetexists for the well, or some combination of these two. In some aspects,a decline curve may be assigned to a well once the periodic productiondeclines from the identified period to the next subsequent period forthe well. For instance, based on such a decline, a maximum productionvalue (e.g., Qi) has been determined for that particular active well.

The illustrated implementation of the allocation model 200 continues atstep 224 as shown in FIG. 2C. In some aspects, steps 226-244 generallydescribe a sub-process within the allocation model 200 that: (i)determines allocated production values for active wells in theidentified period; and (ii) flags or determines “new” wells in theidentified period (e.g., wells associated with the area ID that firstproduced hydrocarbon values in the identified period. Step 224 includessetting pending production data, sales production data, and predictedhydrocarbon production for the identified period to zero. For instance,the allocation model 200 may record or otherwise keep track of pendingproduction (e.g., as reported and included in the hydrocarbon productionrecord 120) or sales production data (e.g., as reported and included inthe hydrocarbon production record 120), or both, for an identifiedperiod, as well as predicted production (e.g., periodic production for aparticular well based on a decline curve assigned to the well). Bysetting these values to zero, initially, and subsequently updating suchvalues (as described below), the allocation model 200 may ensure that asum of allocated production for the one or more active wells in aparticular period does not exceed the reported aggregated hydrocarbonproduction value for that particular period.

The allocation model 200 continues at step 226, which includesidentifying a first active well (among two or more active wells) inidentified period. If the identified active well has pending productionfor the identified period (e.g., as recorded in the hydrocarbonproduction record 120 for the selected area ID), then the pendingproduction is assigned (e.g., allocated) to the identified active wellin step 230. Once the pending production is assigned (e.g., allocated)to the identified active well in step 230, the allocation model 200increases a sum of pending production (initially set to zero in step224) by the amount of assigned pending production in step 232. Turningbriefly to FIG. 5C, graphical illustrations for three wells on the ProstUnit B lease (wells 1H, 2H, and 3H) are illustrated (with the curves 508a, 510 a, and 512 a for wells 1H, 2H, and 3H, respectively) once step232 of the allocation model 200 has been completed for this example area(e.g., lease) and wells on the lease for all periods associated with thelease. Here, pending production assigned to each of the wells 1H, 2H,and 3H, are highlighted with bubble callouts. The 2H well does not reachits maximum production in its first month but in the second month. Thedecline curve for the 3H can be defined in its second month ofproduction, but the 2H is defined in its third month. Had there been newwells beginning production in these months, then the amount assigned tothem would have been proportionally allocated from the difference of thelease-level value and the sum of the pending production values plus thesum of any adjusted predicted values (as described with reference toFIG. 3A). In comparison, curves 508 b, 510 b, and 512 b for wells 1H,2H, and 3H, respectively, show an allocation determined by conventionaltechniques.

Step 232 continues to step 244, in which the allocation model 200determines whether there is an additional active well in the identifiedperiod. If so, then the sub-process shown in FIG. 2C identifies the nextactive well in the identified period in step 245 and loops back to step228 to determine an allocated production for the next identified activewell.

If the identified active well has no pending production for theidentified period (e.g., as recorded in the hydrocarbon productionrecord 120 for the selected area ID) in step 228, then the allocationmodel 200 continues to step 229, and determines whether the identifiedactive well has sales production for the identified period (e.g., asrecorded in the hydrocarbon production record 120 for the selected areaID). If “yes,” then the sales production is assigned (e.g., allocated)to the identified active well in step 231. Once the sales production isassigned (e.g., allocated) to the identified active well in step 231,the allocation model 200 increases a sum of sales production (initiallyset to zero in step 224) by the amount of assigned sales production instep 233. Step 233 continues to step 244, in which the allocation model200 determines whether there is an additional active well in theidentified period. If so, then the sub-process shown in FIG. 2Cidentifies the next active well in the identified period in step 245 andloops back to step 228 to determine an allocated production for the nextidentified active well.

If the identified active well has no pending production and no salesproduction for the identified period (e.g., as recorded in thehydrocarbon production record 120 for the selected area ID) in steps 228and 229, then the allocation model 200 continues to step 234 anddetermines whether the identified active well has an assigned declinecurve. If the identified active well has an assigned decline curve, thena predicted production in the identified period for the identifiedactive well is determined in step 236. For example, the assigned declinecurve may predict what the production of the identified active wellwould be, absent a reported pending production value for that well inthat period.

As described previously, in some cases, a well may have reported pendingproduction (e.g., to a particular regulatory entity) but no reportedsales production. In some cases, a well may have no reported pendingproduction but does have reported sales production (e.g., to aparticular regulatory entity). In some cases, a well may have bothreported pending production and reported sales production. In somecases, a well may have neither reported pending production nor reportedsales production. Turning briefly to FIGS. 7A-7D, these figuresgraphically illustrate one or more effects of steps 229, 231, and 233 inthe overall method 200 shown in FIG. 2. For example, FIGS. 7A-7D showexample allocation curves of allocation volume (in BBL) vs. time period(in months) for four wells in which one or more of the wells may havereported pending production, or reported sales production, or both,during the production time frame. For example, turning first to FIG. 7A,the illustrated graph 700 shows four allocation curves for a particularwell. Allocation curve 702 represents a conventional allocation model,i.e., that does not follow the method 200, or does not account forpending or sales production, or both. Allocation curve 704 represents anallocation model that takes into account pending production dataaccording to method 200, but does not take into account sales productiondata. Allocation curve 706 represents an allocation model that takesinto account both pending and sales production data according to method200. Points 708 represent reported pending or sales production data. Forinstance, if there is reported pending production but no reported salesproduction for a particular time period (e.g., month), then point 712represents the reported pending production. If there is reported pendingproduction and reported sales production for a particular time period(e.g., month), then point 712 represents the reported pending productionaccording to method 200. If there is no reported pending production butthere is reported sales production for a particular time period (e.g.,month), then point 712 represents the reported sales production.Finally, points 712 represent reported pending production (only) for aparticular time period. Here, there are five reported production (i.e.,sales or pending) months.

As further shown in graph 700, an operator reported production curve 710is shown in comparison to the allocation curves. The operator reportedproduction curve 710 represents the known and accurate periodicproduction of the well represented in graph 700 as determined by thewell operator. Thus, curve 710 is shown for comparison purposes to showthe relative accuracy of the allocation curves 702, 704, and 706 againstthe actual production. As shown, allocation curve 706 most closelyfollows the curve 710, showing the curve 706 (which represents theallocation model that takes into account both pending and salesproduction data according to method 200) as the most accurate allocationmodel.

FIG. 7B shows graph 720 that shows four allocation curves for anotherparticular well. Allocation curve 722 represents a conventionalallocation model, i.e., that does not follow the method 200, or does notaccount for pending or sales production, or both. Allocation curve 724represents an allocation model that takes into account pendingproduction data according to method 200, but does not take into accountsales production data. Allocation curve 726 represents an allocationmodel that takes into account both pending and sales production dataaccording to method 200. Points 728 represent reported pending or salesproduction data. For instance, if there is reported pending productionbut no reported sales production for a particular time period (e.g.,month), then point 732 represents the reported pending production. Ifthere is reported pending production and reported sales production for aparticular time period (e.g., month), then point 732 represents thereported pending production according to method 200. If there is noreported pending production but there is reported sales production for aparticular time period (e.g., month), then point 732 represents thereported sales production. Finally, points 732 represent reportedpending production (only) for a particular time period. Here, there areten reported production (i.e., sales or pending) months.

As further shown in graph 720, an operator reported production curve 730is shown in comparison to the allocation curves. The operator reportedproduction curve 730 represents the known and accurate periodicproduction of the well represented in graph 720 as determined by thewell operator. Thus, curve 730 is shown for comparison purposes to showthe relative accuracy of the allocation curves 722, 724, and 726 againstthe actual production. As shown, allocation curve 726 most closelyfollows the curve 730, showing the curve 726 (which represents theallocation model that takes into account both pending and salesproduction data according to method 200) as the most accurate allocationmodel.

FIG. 7C shows graph 740 that shows four allocation curves for anotherparticular well. Allocation curve 742 represents a conventionalallocation model, i.e., that does not follow the method 200, or does notaccount for pending or sales production, or both. Allocation curve 744represents an allocation model that takes into account pendingproduction data according to method 200, but does not take into accountsales production data. Allocation curve 746 represents an allocationmodel that takes into account both pending and sales production dataaccording to method 200. Points 748 represent reported pending or salesproduction data. In this example, there is no pending productionreported and there is only a single point 748 of sales production datareported in February 2015. As further shown in graph 740, an operatorreported production curve 750 is shown in comparison to the allocationcurves. The operator reported production curve 750 represents the knownand accurate periodic production of the well represented in graph 740 asdetermined by the well operator. Thus, curve 750 is shown for comparisonpurposes to show the relative accuracy of the allocation curves 742,744, and 746 against the actual production. As shown, allocation curve746 most closely follows the curve 750, showing the curve 746 (whichrepresents the allocation model that takes into account both pending andsales production data according to method 200) as the most accurateallocation model. In this example, as there is only a single reportedsales production data point, the allocation curves 744 and 746 arelargely determined by the selected decline curve (e.g., in steps 234-238and on) and are, thus quite similar.

Finally, FIG. 7D shows graph 760 that shows four allocation curves foranother particular well. Allocation curve 762 represents a conventionalallocation model, i.e., that does not follow the method 200, or does notaccount for pending or sales production, or both. Allocation curve 764represents an allocation model that takes into account pendingproduction data according to method 200, but does not take into accountsales production data. Allocation curve 766 represents an allocationmodel that takes into account both pending and sales production dataaccording to method 200. Points 768 represent reported pending or salesproduction data. In this example, there is no pending productionreported and there are four points 768 of sales production datareported. As further shown in graph 760, an operator reported productioncurve 770 is shown in comparison to the allocation curves. The operatorreported production curve 770 represents the known and accurate periodicproduction of the well represented in graph 760 as determined by thewell operator. Thus, curve 770 is shown for comparison purposes to showthe relative accuracy of the allocation curves 762, 764, and 766 againstthe actual production. As shown, allocation curve 766 most closelyfollows the curve 770, showing the curve 766 (which represents theallocation model that takes into account both pending and salesproduction data according to method 200) as the most accurate allocationmodel.

In step 238, the allocation model 200 determines whether the identifiedperiod is the first period associated with the area ID (e.g., asrecorded in the hydrocarbon production record 120 of the area ID). Ifthe identified period is not the first period, then the predictedproduction (e.g., from the decline curve) is proportioned according to apredicted production for the identified active well in a previous period(e.g., the immediately previous period to the identified period) in step240. The proportioned predicted production value is then assigned to theidentified active well in step 239.

Turning briefly to FIG. 5D, graphical illustrations for three wells onthe Prost Unit B lease (wells 1H, 2H, and 3H) are illustrated (with thecurves 514 a, 516 a, and 518 a for wells 1H, 2H, and 3H, respectively)once step 240 of the allocation model 200 has been completed for thisexample area (e.g., lease) and wells on the lease for all periodsassociated with the lease. As shown the 1H well was the only wellproducing for the first five months, so it has been assigned the entirelease production during that period. The 2H and 3H wells beganproduction in the same month (April 2013). Based upon the decline curvefor the 1H well (Qi=24,137 bbls), its adjusted predicted value is 10,780bbls. This is subtracted from the lease-level value of 42,258 bbls sothat the 2H and 3H are equally assigned half the difference (15,738bbls). In the next month, using a similar allocation, the 2H and 3H eachreceive 14,269 bbls (37,749 bbls less 9,211 bbls for the 1H divided by2). Because the production value has decreased for both the 2H and the3H wells, a decline curve for them may be defined with Qi=15,738 bbls.In addition to honoring the lease-level monthly values (e.g., the sum ofall allocated values must equal the lease-level values), it may be thatthere are pending production values for certain wells that are alsohonored by the allocation model 200. This is the case for all three ofthese wells. The 1H well has six months of pending production (one moremonth than being the only well in the lease), and the 2H and 3H wellseach have two months of pending production. These pending values arethus included in the allocated production streams shown in thesefigures. In comparison, curves 514 b, 516 b, and 518 b for wells 1H, 2H,and 3H, respectively, show an allocation determined by conventionaltechniques.

If the identified period is the first period as determined in step 238,or once the proportioned predicted production value is then assigned tothe identified active well in step 239, the allocation model 200continues in step 242, which includes increasing a sum of pending/salesproduction (initially set to zero in step 224) by the assigned amountfrom step 239. Again, by updating the sum of a pending or salesproduction value (e.g., depending on the results of steps 228 and 229)and a sum of a predicted production value in the identified period, theallocation model may ensure that a sum of allocated production for theone or more active wells in a particular period does not exceed thereported (e.g., pending or sales) aggregated hydrocarbon productionvalue for that particular period.

Step 242 also continues at step 244, in which the allocation model 200determines whether there is an additional active well in the identifiedperiod. If so, then the sub-process shown in FIG. 2C identifies the nextactive well in the identified period in step 245 and loops back to step228 to determine an allocated production for the next identified activewell.

Returning to step 234, if the identified active well does not have anassigned decline curve (and also does not have either of pendingproduction or sales production for the identified period as determinedin step 228) then the identified active well is flagged as a “new” wellin the identified period in step 246. Step 246 also continues to step244, in which the allocation model 200 determines whether there is anadditional active well in the identified period. If so, then thesub-process shown in FIG. 2C identifies the next active well in theidentified period in step 245 and loops back to step 228 to determine anallocated production for the next identified active well.

If the allocation model 200 determines that there are no additionalactive wells in the identified period in step 244, then the allocationmodel 200 continues to sub-process 300 shown in FIG. 3A. Generally, thesub-process 300 of the allocation model 200: (i) determines allocatedproduction values for “new” wells in the identified period (as shown inFIG. 3A), and (ii) applies any well test data to “new” wells (as shownin FIG. 3B).

The illustrated implementation of the allocation model 200 continues atstep 302, which includes determining a sum of: (i) pending or salesproduction, and (ii) predicted production for the identified period. Asdescribed above, the pending and sales production values and predictedproduction values are initially set to zero (in step 224) and updated(in steps 232, 233, and 242) to account for assigned values toidentified active wells.

If, in step 304, the allocation model 200 determines that the sum isless than or equal to the aggregated hydrocarbon production value (e.g.,from hydrocarbon production record 120) for the identified period, thenthe sum is then subtracted from the aggregated hydrocarbon productionvalue for the identified period in step 310. The difference (e.g., theremainder) is then divided by the number of flagged new wells in theidentified period in step 312. The quotient of the division of step 312is then assigned to each flagged new well in the identified period instep 314.

Step 314 continues to step 316, where the allocation model 200determines whether the assigned amount (from step 314) in the identifiedperiod is less than an amount allocated to the new well in a previousperiod (e.g., an immediately previous period to the identified period).If the determination is “yes,” then the sub-process 300 continues atstep 318 (described in more detail below). If the determination in step316 is “no,” then the sub-process 300 returns to step 222 (e.g., toidentify the next period associated with the area ID).

Returning to step 304, if the allocation model 200 determines that thesum is greater than the aggregated hydrocarbon production value (e.g.,from hydrocarbon production record 120) for the identified period, thenthe predicted production value for each active well in the identifiedperiod is proportionally (e.g., equally) reduced so that the sum isequal to the aggregated hydrocarbon production value. Thus, step 306ensures that the sum of: (i) pending or sales production, and (ii)predicted production for active wells in the identified period does notexceed the reported aggregated area-level hydrocarbon production valuefor the identified period.

Step 306 continues to step 308, which includes assigning no productionto new wells. For instance, if the allocation model 200, afterallocating pending production to active well(s) in the identifiedperiod, determines that the predicted production values for active wellswith assigned decline curves accounts for all of (and possibly morethan) the difference in the aggregated area-level hydrocarbon productionvalue for the identified period and the allocated pending production,then new wells in the identified period receive no allocated or assignedpredicted production. Thus, pending or sales production values, in thisimplementation of the allocation model 200, takes precedence over eitherproportionally allocated values for wells without a decline curve (yetdefined) and over adjusted predicted values for wells with declinecurves. Step 308 continues to step 222 (e.g., to identify the nextperiod associated with the area ID).

Returning to step 316, if the determination is “yes,” then thesub-process 300 continues at step 318, which includes identifying thefirst new well in the identified period. A determination is made at step320 as to whether the identified new well has associated well test data(e.g., from the hydrocarbon production record 120 for the selected areaID). Turning briefly to FIG. 5E, the curve 514 a for the 1H well isshown along with a curve 520 b which represents a conventionalallocation technique used for this well that strictly adheres to welltest data, shown with the larger dots on the curve. Note that thewell-test values on curve 520 b are consistently (but not necessarily)larger than the allocated values for the months with well-test data oncurve 514 a.

If the new well has no associated well test data (e.g., a 24 hour welltest scaled to one month of production) in step 320, then the allocationmodel 200 continues at step 322, which includes assigning the area-leveldecline curve to the identified new well. For example, once a declinecurve is assigned (e.g., based on reservoir geology or a previousdetermination of allocated periodic production values for the wellsassociated with the area ID (e.g., from a previous iteration of theallocation model 200). For instance, in some aspects, the hydrocarbonproduction record 120 includes a decline curve model, for example, basedon known reservoir information (e.g., geologic information of a knownrock formation). Turning briefly to FIGS. 5A and 5B, the graphs 500 and550, respectively, show area-level decline curves are shown for the tenwells on the Prost Unit B Lease.

Step 322 continues to step 324, which includes adjusting the assignedcurve maximum initial production (Qi) for the identified new well to theallocated amount in the previous period (as described in step 316).

If the new well has associated well test data (e.g., a 24 hour well testscaled to one month of production) in step 320, then the allocationmodel 200 continues at step 326, which includes fitting a decline curveto the identified new well based on allocated amount in previous periodsand the associated well test data. Turning briefly to FIG. 5F, a newcurve 520 a for the 1H well is shown that takes into account well testdata for this well. Note that the curve 520 a does not exactly fit thewell-test values, but the overall curve 520 a has been “raised” inaccordance with the well test data. The production stream for the 1Hwell now has been allocated honoring pending production and conditionedwith the well-test data. The aggregated lease-level production values(e.g., from the reported production) are also honored in curve 520 a.

Steps 324 and 326 continue to step 328, and a determination is madewhether there are any additional new wells in the identified period. Ifthe determination is “yes,” then step 328 loops back to step 320. If thedetermination is “no,” then the allocation model 200 returns to step 222(e.g., to identify the next period associated with the area ID).

Returning to step 220, if the identified period is the last periodassociated with the area ID, then the allocation model 200 continues inan iterative process through sub-process 400, shown in FIG. 4. Forexample, in some aspects, the allocation model 200 is iterativelyexecuted to a desired or specified convergence. For example, with eachiteration, a particular metric may be determined and compared against aspecified or desired threshold of that metric. The metric may be, forexample, an absolute average change in periodic production averaged overall producing periods for all wells. As another example, the metric maybe a sum of squared changes in periodic production averaged over allproducing periods for all wells. If the metric is greater than thethreshold, the iterative process may continue (e.g., allocation model200 may be iteratively executed). In some aspects, even if the thresholdis not met, the iterative process may be limited to a maximum number ofiterations (e.g., by the user or operator of the allocation modelservice). If the metric is less than the threshold, the iterativeprocess may terminate and retain the determined, well-by-well periodicallocated production values produced in the previous iteration. Suchretained values may be transmitted to or displayed at the client devices102, 104, 106, 108, and/or 110 from the server system 112.

Step 220 thus continues to step 402, which includes identifyingallocated production for each well associated with the area ID. As anexample of what the identified allocated production values appear,graphically, turning to FIG. 6, this figure shows plots 600 and 650 thatrepresent allocated production streams (e.g., first iteration, lastiteration, and one or more intermediate iterations) for two wells (6Hand 32H) out of 126 wells in the Briscoe Ranch Cochina East Ranch leasein Dimmit County, Tex. The initial allocated streams (graph 600 for the6H well and graph 650 for the 32H well) are shown with plots withcircular points and the final allocated streams with plots with squarepoints. The smooth plots represent the allocated streams in intermediateiterations between the first and last iteration (e.g., as shown, a totalof six iterations to convergence).

If the allocation model 200 has executed only once, then the number ofiterations is increased by 1 in step 412. In step 414, the identifiedallocation production values are shifted for each well to a commoninitial period. For example, all the allocated production streams(determined from the first iteration of the allocation model 200) aretime-shifting to month “0.” Turning briefly to FIG. 5I, these shiftedallocated values are shown graphically. By doing so, the allocatedproduction streams, on a well-by-well basis, appear to all beginproduction at the same initial period (e.g., the same month) even thoughthey may not have, in reality, began production in the same period.

The initial period may also be defined as period in which maximum or“peak” production occurs. Using this period as an initial period insteadof period “0” (e.g., month 0) may provide better results for the declinecurve fitting. For example, turning briefly to FIGS. 6A-6B, thesefigures show a difference between shifting the allocated values to aperiod “0” (e.g., month “0” of the area ID) and shifting the allocatedvalues to an initial period which represents the maximum productionvalue (Qi) for each well in the area ID. FIG. 5A shows an example curve505 in which the allocated values are shifted to a common period “0”(e.g., a first period in which production was reported for an area ID).FIG. 5A shows the generation of the Prost Unit B type curve from the tenproducing wells on this lease. The curve 505 is the result of eachindividual well stream being referenced to the first month of productionfor this lease and then the monthly values averaged by the number ofproducing wells in each month (shown in the histogram 510). As shown inthis example, the deterministic decline curve (in this example, an Arp'sequation decline curve), has parameters of Qi, Di, and b for the curve500 as shown.

FIG. 5B shows the generation of the Prost Unit B type curve from the tenproducing wells on this lease when each individual well stream isreferenced to an initial period “0” which represents the period for eachrespective well in which the maximum production (Qi) for that respectivewell was reported. In this figure, the curve 555 is the result of eachindividual well stream being referenced to the peak month of itsrespective production for this lease, and then the monthly valuesaveraged by the number of producing wells in each month (shown in thehistogram 560). Note the parameters of the Arp's model fit to the typecurve and the differences with the type curve referenced to the firstmonth of production. As shown in this example, the deterministic declinecurve (in this example, an Arp's equation decline curve), has parametersof Qi, Di, and b for the curve 550 as shown.

In step 416, the shifted allocated production values are aggregated toform a set of aggregated periodic hydrocarbon production values for theselected area ID. The set of aggregated periodic hydrocarbon productionvalues for the selected area ID from step 416 are then normalized by thenumber of active wells in each period. For example, the aggregatedproduction value for each period (e.g., shifted period) may be dividedby the number of active wells in that period to arrive at a set ofnormalized production values for the production periods.

This normalized set, in some aspects, may mimic, or serve as asubstitute for the reported aggregated hydrocarbon production values inthe hydrocarbon production record 120 associated with the area ID. Thisset may thus serve as a substitute for such reported aggregatedhydrocarbon production values in the hydrocarbon production record 120associated with the area ID in step 204 in a subsequent (e.g., notfirst) iteration of the allocation model 200. Looking again at FIGS.6A-6B, these figures show two example techniques for normalizing theaggregated hydrocarbon production values. As noted, FIG. 6A shows anexample in graph 505 in which the allocated values are shifted to acommon period “0” (e.g., a first period in which production was reportedfor an area ID). FIG. 6B shows an example in graph 555 where thegeneration of the Prost Unit B type curve from the ten producing wellson this lease when each individual well stream is referenced to aninitial period “0” which represents the period for each respective wellin which the maximum production (Qi) for that respective well wasreported.

Returning to step 404, if the allocation model 200 has executed morethan once, the identified allocated production for each well associatedwith the area ID from the current iteration is compared againstallocated production for each well associated with the area ID from theprevious iteration. For example, if the identified values in step 402are from a third iteration of the allocation model 200, then the valuesstored (e.g., in the data store 118) from the second iteration arecompared. By comparison, for instance, a metric representative of eachset of allocated production values may be compared, such as absoluteaverage change in periodic production averaged over all producingperiods for all wells, a sum of squared changes in periodic productionaveraged over all producing periods for all wells, or another specifiedmetric. For example, turning to FIG. 6, this figure shows plots 600 and650 that represent allocated production streams (e.g., first iteration,last iteration, and one or more intermediate iterations) for two wells(6H and 32H) out of 126 wells in the Briscoe Ranch Cochina East Ranchlease in Dimmit County, Tex. The initial allocated streams (graph 600for the 6H well and graph 650 for the 32H well) are shown with plotswith circular points and the final allocated streams with plots withsquare points. The smooth plots represent the allocated streams inintermediate iterations between the first and last iteration (e.g., asshown, a total of six iterations to convergence). Note that in thisexample, the allocation model 200 comes close to the final allocatedstream in the first iteration (the second pass through the model 200).Iterations two through six improve on the first iteration, which reducedthe absolute error by nearly 95% relative to the initial execution ofthe model 200.

If, in step 408, a determination is made that the metric does not meet(e.g., greater than) a threshold metric value, then step 408 continuesto steps 412-418, as described above. If, however, in step 408, thedetermination is made that the metric does meet (e.g., less than) thethreshold metric value, then the currently identified allocatedproduction values for each well associated with the area ID may beoutput, e.g., to a client device, in step 410.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, various formsof the flows shown above may be used, with steps re-ordered, added, orremoved. Accordingly, other implementations are within the scope of thefollowing claims.

Implementations of the present disclosure and all of the functionaloperations provided herein can be realized in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Implementationsof the allocation model and allocation model service can be realized asone or more computer program products, e.g., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing apparatus” encompassesall apparatus, devices, and machines for processing data, including byway of example a programmable processor, a computer, or multipleprocessors or computers. The apparatus can include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this disclose can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, implementations of the inventioncan be implemented on a computer having a display device, e.g., a CRT(cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Implementations of the invention can be realized in a computing systemthat includes a back end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this disclosure contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this disclosure in the context of separate implementationscan also be provided in combination in a single implementation.Conversely, various features that are described in the context of asingle implementation can also be provided in multiple implementationsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variation ofa sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the present disclosure have beendescribed. Other implementations are within the scope of the followingclaims. For example, the actions recited in the claims can be performedin a different order and still achieve desirable results.

1. (canceled)
 2. An apparatus comprising at least one tangible,non-transitory computer readable media that comprises instructionsexecutable by one or more hardware processors to perform operations,comprising: (i) receiving, from a client device communicably coupled toa server, a selection of a particular area identification (ID) of aplurality of area IDs stored on the server; (ii) determining, based onthe selected particular area ID, a plurality of hydrocarbon productionvalues that comprise periodic area-level hydrocarbon production valuesassociated with the particular area ID and a plurality of wellsassociated with the particular area ID; (iii) determining a declinecurve model for the area-level hydrocarbon production values associatedwith the particular area ID; (iv) modeling the aggregated periodicwell-level hydrocarbon production values with the determined declinecurve model, the modeling comprising: determining a first period of anumber of periods, the first period associated with a first area-levelhydrocarbon production value, selecting a period of the number ofperiods, starting with the first period and ending with a last period ofthe number of periods, and for the selected period, determining a numberof active wells associated with the particular area ID and based on thenumber of active wells being more than one active well, for each activewell in the selected period: determining that the active well has atleast one of pending production or sales production in the selectedperiod, and based on the active well having the at least one of pendingproduction or sales production in the selected period, assigning the atleast one of pending production or sales production to the active well;(v) determining allocated well-level hydrocarbon production values basedat least in part on the selected decline curve model to display at theclient device; and generating a user-viewable output file that comprisesthe determined allocated well-level hydrocarbon data values from step(v).
 3. The apparatus of claim 2, wherein the operation of determining adecline curve model for the area-level hydrocarbon production valuesassociated with the particular area ID comprises: determining thedecline curve model for the area-level hydrocarbon production valuesassociated with the particular area ID based, at least in part, on ageology of a reservoir associated with the area ID.
 4. The apparatus ofclaim 2, wherein the operation of determining a decline curve model forthe area-level hydrocarbon production values associated with theparticular area ID based, at least in part, on a geology of a reservoirassociated with the area ID comprises: (vi) determining allocatedperiodic well-level hydrocarbon production values for each of theplurality of wells associated with the particular area ID; (vii)shifting the allocated periodic well-level hydrocarbon production valuesfor each of the plurality of wells to an initial time period; (viii)aggregating the shifted allocated periodic well-level hydrocarbonproduction values to generate aggregated periodic area-level hydrocarbonproduction values; and (ix) determining the decline curve model for thearea-level hydrocarbon production values associated with the particulararea ID based on the aggregated periodic well-level hydrocarbonproduction values.
 5. The apparatus of claim 2, wherein the operationsfurther comprise: determining the last period of the number of periods,the last period associated with a last area-level hydrocarbon productionvalue.
 6. The apparatus of claim 5, wherein the operations furthercomprise: for the selected period, determining a total number of wellsassociated with the particular area ID.
 7. The apparatus of claim 6,wherein the operations further comprise, based on the number of activewells being one active well in the selected period, assigning anarea-level hydrocarbon production value in the selected period to theone active well.
 8. The apparatus of claim 2, wherein the operationsfurther comprise: determining that the active well has no pendingproduction and no sales production in the selected period; based on theactive well having no pending production and no sales production in theselected period, determining that the active well has an assigneddecline curve model; and based on the active well having the assigneddecline curve model, determining a predicted production for the selectedperiod for the active well.
 9. The apparatus of claim 8, wherein theoperations further comprise: based on the selected period beingsubsequent to the first period, determining that the active well isassociated with a predicted production from the assigned decline curvemodel from a previous period in the number of periods; proportioning thepredicted production of the active well for the selected period based onthe predicted production of the active well for the previous period; andassigning the proportioned predicted production to the active well forthe selected period.
 10. The apparatus of claim 2, wherein theoperations further comprise: determining that the active well has nopending production and no sales production in the selected period and noassigned decline curve model; and based on the determination, flaggingthe active well as a new well for the selected period.
 11. The apparatusof claim 10, wherein the operations further comprise, for each new wellin the selected period: determining a sum of: (a) pending production forthe active wells in the selected period or sales production for theactive wells in the selected period, and (b) the predicted productionfor the active wells in the selected period; determining that the sum isgreater than the area-level hydrocarbon production value for theselected period; equalizing the sum of (a) and (b) with the area-levelhydrocarbon production value for the selected period; and assigning zeroproduction to each new well for the selected period.
 12. The apparatusof claim 11, wherein the operations further comprise: determining thatthe sum of (a) and (b) is less than the area-level hydrocarbonproduction value for the selected period; and determining a differencebetween the sum of predicted production for the active wells in theselected period and the predicted production for the active wells in theselected period and the area-level hydrocarbon production value for theselected period; and assigning to each of the new wells in the selectedperiod, a proportional hydrocarbon production value based on thedifference.
 13. The apparatus of claim 12, wherein the operationsfurther comprise, for each new well in the selected period: determiningthat the assigned proportional hydrocarbon production value to the newwell in the selected period is less than an assigned proportionalhydrocarbon production value to the new well in a previous period;identifying well test data associated with the new well; and based onthe identified well test data, fitting the decline curve model to thenew well based at least in part on the well test data and the assignedproportional hydrocarbon production values of the new well in theselected period and the previous period.
 14. The apparatus of claim 13,wherein the operations further comprise: identifying no well test dataassociated with the new well; and based on the identification of no welltest data associated with the new well, fitting the decline curve modelto the new well.
 15. The apparatus of claim 14, wherein the operationsfurther comprise adjusting the decline curve model for the new wellbased, at least in part, on the assigned proportional hydrocarbonproduction value of the new well in the previous period.
 16. Theapparatus of claim 9, wherein the operations further comprise:identifying no new wells in the selected period; and based on theidentification of no new wells in the selected period and based on theselected period being the first period, assigning a proportionedpredicted production to the active well for the selected period, theproportioned predicted production based on the decline curve model andthe number of active wells.
 17. The apparatus of claim 2, wherein thetime period comprises a month.
 18. The apparatus of claim 2, wherein thedecline curve model comprises an Arp's equation decline curve model. 19.The apparatus of claim 2, wherein the decline curve model is defined, atleast in part, by a maximum periodic hydrocarbon production value and atleast one decline rate.
 20. The apparatus of claim 19, wherein the atleast one decline rate comprises an initial decline rate and a declinerate over time.
 21. The apparatus of claim 4, wherein the operationsfurther comprise: performing an iterative process of determining theallocated well-level hydrocarbon production values by iterating steps(vi)-(ix).
 22. The apparatus of claim 21, wherein the operation ofiterating steps (vi)-(ix) comprises: determining new allocated periodicwell-level hydrocarbon production values for each of the plurality ofwells based on the determined allocated well-level hydrocarbonproduction values in a previous iteration of step (v); shifting the newallocated periodic well-level hydrocarbon production values for each ofthe plurality of wells to a first period of a number of periodsassociated with the particular area ID; aggregating the shifted newallocated periodic well-level hydrocarbon production values to generatenew aggregated periodic area-level hydrocarbon production values; anddetermining a new decline curve model for the new aggregated periodicarea-level hydrocarbon production values.
 23. The apparatus of claim 1,wherein an area ID comprises a lease ID.